Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (22): 282-291.DOI: 10.3778/j.issn.1002-8331.2307-0335
• Graphics and Image Processing • Previous Articles Next Articles
LIN Haotian, LI Yongchang, JIANG Jing, QIN Guangjun
Online:
2024-11-15
Published:
2024-11-14
林浩田,李永昌,江静,秦广军
LIN Haotian, LI Yongchang, JIANG Jing, QIN Guangjun. Lightweight Full-Flow Bidirectional Fusion Network for 6D Pose Estimation[J]. Computer Engineering and Applications, 2024, 60(22): 282-291.
林浩田, 李永昌, 江静, 秦广军. 用于6D姿态估计的轻量级全流双向融合网络[J]. 计算机工程与应用, 2024, 60(22): 282-291.
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[1] DENG X, XIANG Y, MOUSAVIAN A, et al. Self-supervised 6D object pose estimation for robot manipulation[C]//Proceedings of the 2020 IEEE International Conference on Robotics and Automation, Paris, 2020: 3665-3671. [2] SU Y, RAMBACH J, MINASKAN N, et al. Deep multi-state object pose estimation for augmented reality assembly[C]//Proceedings of the 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct, Beijing, 2019: 222-227. [3] WU D, ZHUANG Z, XIANG C, et al. 6D-VNet: end-to-end 6DoF vehicle pose estimation from monocular RGB images[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, 2019: 1238-1247. [4] XIANG Y, SCHMIDT T, NARAYANAN V, et al. PoseCNN: a convolutional neural network for 6d object pose estimation in cluttered scenes[J]. arXiv:1711.00199, 2017. [5] ZAKHAROV S, SHUGUROV I, ILIC S. DPOD: 6D pose object detector and refiner[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, Seoul, 2019: 1941-1950. [6] WANG C, XU D, ZHU Y, et al. DenseFusion: 6D object pose estimation by iterative dense fusion[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, 2019: 3338-3347. [7] HE Y, SUN W, HUANG H, et al. PVN3D: a deep point-wise 3D keypoints voting network for 6dof pose estimation[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 11629-11638. [8] HE Y, HUANG H, FAN H, et al. FFB6D: a full flow bidirectional fusion network for 6D pose estimation[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, 2021: 3002-3012. [9] CHEN J, KAO S, HE H, et al. Run, don’t walk: chasing higher FLOPS for faster neural networks[C]//Proceedings of the 2023 IEEE/CVF conference on Computer Vision and Pattern Recognition, 2023: 12021-12031. [10] YU W, LUO M, ZHOU P, et al. MetaFormer is actually what you need for vision[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, 2022: 10809-10819. [11] CALLI B, SINGH A, WALSMAN A, et al. The YCB object and model set: towards common benchmarks for manipulation research[C]//Proceedings of the 2015 International Conference on Advanced Robotics, Istanbul, 2015: 510-517. [12] HINTERSTOISSER S, LEPETIT V, ILIC S, et al. Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes[C]//Proceedings of the 11th Asian Conference on Computer Vision. Berlin, Heidelberg: Springer, 2013: 548-562. [13] KEHL W, MANHARDT F, TOMBARI F, et al. SSD-6D: making RGB-based 3D detection and 6D pose estimation great again[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, 2017: 1530-1538. [14] DO T T, CAI M, PHAM T, et al. Deep-6DPose: recovering 6D object pose from a single RGB image[J]. arXiv:1802.10367, 2018. [15] LI Z, WANG G, JI X. CDPN: coordinates-based disentangled pose network for real-time RGB-based 6D of object pose estimation[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, Seoul, 2019: 7677-7686. [16] PARK K, MOUSAVIAN A, XIANG Y, et al. LatentFusion: end-to-end differentiable reconstruction and rendering for unseen object pose estimation[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 10707-10716. [17] 梁达勇, 陈俊洪, 朱展模, 等. 多特征像素级融合的遮挡物体6DoF姿态估计研究[J]. 计算机科学与探索, 2020, 14(12): 2072-2082. LIANG D Y, CHEN J H, ZHU Z M, et al. Research on occluded objects 6DoF pose estimation with multi-feature and pixel-level fusion[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(12): 2072-2082. [18] SARODE V, LI X, GOFORTH H, et al. PCRNet: point cloud registration network using pointnet encoding[J]. arXiv:1908. 07906, 2019. [19] CHEN W, JIA X, CHANG H J, et al. G2L-Net: global to local network for real-time 6D pose estimation with embedding vector features[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 4232-4241. [20] AOKI Y, GOFORTH H, SRIVATSAN R A, et al. PointNetLK: robust & efficient point cloud registration using PointNet[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, 2019: 7156-7165. [21] GAO G, LAURI M, WANG Y, et al. 6D object pose regression via supervised learning on point clouds[C]//Proceedings of the 2020 IEEE International Conference on Robotics and Automation, Paris, 2020: 3643-3649. [22] PHAM Q H, UY M A, HUA B S, et al. LCD: learned cross-domain descriptors for 2D-3D matching[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020: 11856-11864. [23] LIU X, JONSCHKOWSKI R, ANGELOVA A, et al. KeyPose: multi-view 3D labeling and keypoint estimation for transparent objects[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 11599-11607. [24] SONG C, SONG J, HUANG Q. HybridPose: 6D object pose estimation under hybrid representations[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 428-437. [25] HODAN T, BARATH D, MATAS J. EPOS: estimating 6D pose of objects with symmetries[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 11700-11709. [26] 包志强, 邢瑜, 吕少卿, 等. 改进YOLO V2的6D目标姿态估计算法[J]. 计算机工程与应用, 2021, 57(9): 148-153. BAO Z Q, XING Y, LYU S Q, et al. Improved YOLO V2 6D object pose estimation algorithm[J]. Computer Engineering and Applications, 2021, 57(9): 148-153. [27] 李冬冬, 郑河荣, 刘复昌, 等. 结合掩码定位和漏斗网络的6D姿态估计[J]. 中国图象图形学报, 2022, 27(2): 642-652. LI D D, ZHENG H R, LIU F C, et al. 6D pose estimation based on mask location and hourglass network[J]. Journal of Image and Graphics, 2022, 27(2): 642-652. [28] ZENG A, SONG S, NIESSNER M, et al. 3DMatch: learning local geometric descriptors from RGB-D reconstructions[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017: 199-208. [29] YEW Z J, LEE G H. 3DFeat-Net: weakly supervised local 3D features for point cloud registration[C]//Proceedings of the 15th European Conference on Computer Vision. Cham: Springer, 2018: 630-646. [30] FISCHER K, SIMON M, OLSNER F, et al. StickyPillars: robust and efficient feature matching on point clouds using graph neural networks[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, 2021: 313-323. [31] CRIVELLARO A, RAD M, VERDIE Y, et al. Robust 3D object tracking from monocular images using stable parts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(6): 1465-1479. [32] WADA K, SUCAR E, JAMES S, et al. MoreFusion: multi-object reasoning for 6D pose estimation from volumetric fusion[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 14528-14537. [33] KUMAR A, SHUKLA P, KUSHWAHA V, et al. Context-aware 6D pose estimation of known objects using RGB-D data[J]. arXiv:2212.05560, 2022. [34] JIANG X, LI D, CHEN H, et al. Uni6D: a unified CNN framework without projection breakdown for 6D pose estimation[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, 2022: 11164-11174. [35] SUN M, ZHENG Y, BAO T, et al. Uni6Dv2: noise elimination for 6D pose estimation[C]//Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, 2023: 1832-1844. [36] PENG S, ZHOU X, LIU Y, et al. PVNet: pixel-wise voting network for 6DoF object pose estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6): 3212-3223. [37] 马康哲, 皮家甜, 熊周兵, 等. 融合注意力特征的遮挡物体6D姿态估计[J]. 计算机应用, 2022, 42(12): 3715-3722. MA K Z, PI J T, XIONG Z B, et al. 6D pose estimation incorporating attentional features for occluded objects[J]. Journal of Computer Applications, 2022, 42(12): 3715-3722. [38] GONZALEZ M, KACETE A, MURIENNE A, et al. L6DNet: light 6DoF network for robust and precise object pose estimation with small datasets[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2914-2921. [39] WANG C, MARTIN-MARTIN R, XU D, et al. 6-PACK: category-level 6D pose tracker with anchor-based keypoints[C]//Proceedings of the 2020 IEEE International Conference on Robotics and Automation, Paris, 2020: 10059-10066. [40] HOWARD A G, ZHU M, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017. [41] ZHANG X, ZHOU X, LIN M, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 6848-6856. [42] HAN K, WANG Y, TIAN Q, et al. GhostNet: more features from cheap operations[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 1577-1586. [43] TAN M, LE Q. EfficientNet: rethinking model scaling for convolutional neural networks[C]//Proceedings of the 36th International Conference on Machine Learning, 2019: 6105-6114. [44] HAN K, WANG Y, ZHANG Q, et al. Model Rubik’s cube: twisting resolution, depth and width for TinyNets[C]//Advances in Neural Information Processing Systems 33, 2020: 19353-19364. [45] GUO M H, CAI J X, LIU Z N, et al. PCT: point cloud transformer[J]. Computational Visual Media, 2021, 7(2): 187-199. [46] ZHAO H, JIANG L, JIA J, et al. Point transformer[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, Montreal, 2021: 16239-16248. [47] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017: 6230-6239. [48] HU Q, YANG B, XIE L, et al. RandLA-Net: efficient semantic segmentation of large-scale point clouds[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 11105-11114. [49] DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009: 248-255. [50] LIANG M, YANG B, WANG S, et al. Deep continuous fusion for multi-sensor 3D object detection[C]//Proceedings of the 15th European Conference on Computer Vision. Cham: Springer, 2018: 663-678. [51] XU D, ANGUELOV D, JAIN A. PointFusion: deep sensor fusion for 3D bounding box estimation[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 244-253. |
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